Story generation is experiencing a revival, despite disappointing preliminary results from the preceding three decades. One of the principle reasons for previous inadequacies was the low level of writing quality, which resulted from the excessive focus of story grammars on plot design. Although these systems leveraged narrative theory via corpora analyses, they failed to thoroughly extend those analyses to all relevant linguistic levels. The end result was narratives that were recognizable as stories, but whose prose quality was unsatisfactory.

This paper brings a logical perspective to the generation of referring expressions, addressing the incompleteness of existing algorithms in this area. After studying references to individual objects, we discuss references to sets, including Boolean descriptions that make use of negated and disjoined properties. To guarantee that a distinguishing description is generated whenever such descriptions exist, the paper proposes generalizations and extensions of the Incremental Algorithm of Dale and Reiter (1995). 1. Introduction Generation of referring expressions (GRE) is a key task of most Natural Language Generation (NLG) systems (e.g., Reiter and Dale (2000), section 5.4). Regardless of the type of Knowledge Base (KB) forming the input to the generator, many objects will tend to be designated in it using the technical jargon of computerized databases. Such jargon can be difficult to understand. This is true, for example,

... document structure can be seen as an extension of Nunberg's `text-grammar'; it is also closely related to `logical' mark-up in languages like HTML and LATEX. We show that by using this intermediate representation, several subtasks in language generation and language understanding can be defined more cleanly.

This paper describes an implemented system which uses centering theory for planning of coherent texts and choice of referring expressions. We argue that text and sentence planning need to be driven in part by the goal of maintaining referential continuity and thereby facilitating pronoun resolution: obtaining a favourable ordering of clauses, and of arguments within clauses, is likely to increase opportunities for non-ambiguous pronoun use. Centering theory provides the basis for such an integrated approach. Generating coherent texts according to centering theory is treated as a constraint satisfaction problem.

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We describe SelTex a text generation system for producing short texts and captions to accompany information graphics that are generated according to the writer's intentions. SelTex uses rules that were extracted from a corpus study of more than 400 text excerpts. This corpus study shows that text and graphics play complementary roles in transmitting information from the writer to the reader. We then derive some observations for the automatic generation of texts associated with graphics many of which were implemented in SelTex.

In this paper we describe a two-stage

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The RAGS proposals for generic specification of NLG systems includes a detailed account of data representation, but only an outline view of processing aspects. In this paper we introduce a modular processing architecture with a concrete implementation which aims to meet the RAGS goals of transparency and reusability. We illustrate the model with the RICHES system -- a generation system built from simple linguistically-motivated modules.

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